【开发实录】基于YOLOv5+DeepSort的行人监控电子围栏系统

1. 项目目标：

本项目目标为开发基于YOLOv5+DeepSort的行人监控电子围栏系统，功能包括：

1. 行人检测和追踪；
2. 危险区域鉴定；
3. 爬墙检测；
4. 长时间逗留检测；
5. 行人聚集检测；

项目使用PyQt5进行软件界面开发。

2. 项目演示：

Bilibili：

基于YOLOv5+DeepSort的行人监控电子围栏系统 V2.0

软件截图：

3. YOLOv5目标检测+DeepSort目标追踪：

代码地址：

https://github.com/Sharpiless/Yolov5-deepsort-inference

如何使用YOLOv5训练自己的数据集请看这篇：

【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）

训练好后，放到weights文件夹下：

编写检测类：

• 基础检测器

from .tracker_deep import update_tracker
import cv2


class baseDet(object):

    def __init__(self, tracker_type):

        self.img_size = 640
        self.threshold = 0.4
        self.max_frame = 160
        self.stride = 2
        self.illegal_num = 0
        self.tracker_type = tracker_type

    def build_config(self):

        self.faceTracker = {
    
    }
        self.illegals = []
        self.faceClasses = {
    
    }
        self.faceregister = {
    
    }
        self.faceLocation1 = {
    
    }
        self.faceLocation2 = {
    
    }
        self.frameCounter = 0
        self.currentCarID = 0
        self.recorded = []

        self.font = cv2.FONT_HERSHEY_SIMPLEX

    def feedCap(self, im, isChecked, region_bbox2draw=None):

        retDict = {
    
    
            'frame': None,
            'faces': None,
            'list_of_ids': None,
            'face_bboxes': []
        }
        self.frameCounter += 1

        im, faces, face_bboxes = update_tracker(
            self, im, region_bbox2draw, isChecked)

        retDict['frame'] = im
        retDict['faces'] = faces
        retDict['face_bboxes'] = face_bboxes

        return retDict

    def init_model(self):
        raise EOFError("Undefined model type.")

    def preprocess(self):
        raise EOFError("Undefined model type.")

    def detect(self):
        raise EOFError("Undefined model type.")

• YOLOv5检测器：

import torch
import numpy as np
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, letterbox
from utils.torch_utils import select_device
from .BaseDetector import baseDet


class Detector(baseDet):

    def __init__(self, tracker_type):
        super(Detector, self).__init__(tracker_type)
        self.init_model()
        self.build_config()

    def init_model(self):

        self.weights = 'weights/final.pt'
        self.device = '0' if torch.cuda.is_available() else 'cpu'
        self.device = select_device(self.device)
        model = attempt_load(self.weights, map_location=self.device)
        model.to(self.device).eval()
        model.half()
        # torch.save(model, 'test.pt')
        self.m = model
        self.names = model.module.names if hasattr(
            model, 'module') else model.names

    def preprocess(self, img):

        img0 = img.copy()
        img = letterbox(img, new_shape=self.img_size)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)
        img = np.ascontiguousarray(img)
        img = torch.from_numpy(img).to(self.device)
        img = img.half()  # 半精度
        img /= 255.0  # 图像归一化
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        return img0, img

    def detect(self, im):

        im0, img = self.preprocess(im)

        pred = self.m(img, augment=False)[0]
        pred = pred.float()
        pred = non_max_suppression(pred, self.threshold, 0.3)

        pred_boxes = []
        for det in pred:

            if det is not None and len(det):
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape).round()

                for *x, conf, cls_id in det:
                    lbl = self.names[int(cls_id)]
                    if not lbl == 'person':
                        continue
                    x1, y1 = int(x[0]), int(x[1])
                    x2, y2 = int(x[2]), int(x[3])
                    pred_boxes.append(
                        (x1, y1, x2, y2, lbl, conf))

        return im, pred_boxes

检测效果：

4. UI界面开发：

4.1 登录界面：

这里写一个简单的登录界面：

import sys
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QPixmap, QFont, QIcon
from PyQt5.QtWidgets import QWidget, QApplication, QLabel, QDesktopWidget, QHBoxLayout, QFormLayout, \
    QPushButton, QLineEdit, QMainWindow


class LoginForm(QMainWindow):

    def __init__(self):
        super().__init__()
        self.initUI()

    def initUI(self):
        """
        初始化UI
        :return:
        """
        self.setObjectName("loginWindow")
        self.setStyleSheet('#loginWindow{background-color:white}')
        self.setFixedSize(650, 300)
        self.setWindowTitle("登录")
        self.setWindowIcon(QIcon('data/logo.png'))

        # 登录表单内容部分
        login_widget = QWidget(self)
        login_widget.move(0, 0)
        login_widget.setGeometry(0, 0, 650, 260)

        hbox = QHBoxLayout()
        # 添加左侧logo
        logolb = QLabel(self)
        logopix = QPixmap("web/logo.png")
        logolb.setPixmap(logopix)
        logolb.setAlignment(Qt.AlignCenter)
        hbox.addWidget(logolb, 1)
        # 添加右侧表单
        fmlayout = QFormLayout()
        lbl_workerid = QLabel("用户名")
        lbl_workerid.setFont(QFont("Microsoft YaHei"))
        led_workerid = QLineEdit()
        led_workerid.setFixedWidth(270)
        led_workerid.setFixedHeight(38)

        lbl_pwd = QLabel("密码")
        lbl_pwd.setFont(QFont("Microsoft YaHei"))
        led_pwd = QLineEdit()
        led_pwd.setEchoMode(QLineEdit.Password)
        led_pwd.setFixedWidth(270)
        led_pwd.setFixedHeight(38)

        self.btn_login = QPushButton("登录")
        self.btn_login.setFixedWidth(270)
        self.btn_login.setFixedHeight(40)
        self.btn_login.setFont(QFont("Microsoft YaHei"))
        self.btn_login.setObjectName("login_btn")
        self.btn_login.setStyleSheet(
            "#login_btn{background-color:#2c7adf;color:#fff;border:none;border-radius:4px;}")
        self.btn_login.clicked.connect(
            lambda : self.close_logui(led_workerid.text(), led_pwd.text())
        )

        fmlayout.addRow(lbl_workerid, led_workerid)
        fmlayout.addRow(lbl_pwd, led_pwd)
        fmlayout.addWidget(self.btn_login)
        hbox.setAlignment(Qt.AlignCenter)
        # 调整间距
        fmlayout.setHorizontalSpacing(20)
        fmlayout.setVerticalSpacing(12)

        hbox.addLayout(fmlayout, 2)

        login_widget.setLayout(hbox)

        self.center()
        self.show()

    def close_logui(self, user, password):
        print('-[INFO] User:{} Password:{}'.format(user, password))
        self.close()

    def center(self):
        qr = self.frameGeometry()
        cp = QDesktopWidget().availableGeometry().center()
        qr.moveCenter(cp)
        self.move(qr.topLeft())


if __name__ == "__main__":
    app = QApplication(sys.argv)
    ex = LoginForm()
    sys.exit(app.exec_())

效果：

4.2 加载界面：

由于 yolov5 模型加载较慢，所以我们需要写一个加载进度界面，核心代码：

def load_data(self, sp):
        for i in range(1, 11):  # 模拟主程序加载过程
            time.sleep(0.5)                   # 加载数据
            sp.showMessage("加载... {0}%".format(
                i * 10), QtCore.Qt.AlignHCenter | QtCore.Qt.AlignBottom, QtCore.Qt.black)
            QtWidgets.qApp.processEvents()  # 允许主进程处理事件

def main(opt):
    '''
    启动PyQt5程序，打开GUI界面
    '''
    app = QApplication(sys.argv)
    splash = QtWidgets.QSplashScreen(QtGui.QPixmap("data/logo.png"))
    splash.showMessage("加载... 0%", QtCore.Qt.AlignHCenter, QtCore.Qt.black)
    splash.show()                           # 显示启动界面
    QtWidgets.qApp.processEvents()          # 处理主进程事件
    main_window = MainWindow(opt)
    main_window.load_data(splash)           # 加载数据
    # main_window.showFullScreen()          # 全拼显示
    app.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())
    splash.close()
    main_window.show()
    sys.exit(app.exec_())

效果：

4.3 主界面：

主界面首先使用Qt Designer进行布局设计：

完成后导出为 .ui 文件：

然后完成另外一些部件，如左侧行人信息栏、详细信息模块等：

然后使用 PyQt5 加载这些文件到代码中，例如：

from PyQt5 import QtWidgets
from PyQt5.QtWidgets import QMainWindow
from PyQt5.uic import loadUi

class DetailLogWindow(QMainWindow):
    def __init__(self, data, parent=None):
        super(DetailLogWindow, self).__init__(parent)
        loadUi("./data/UI/DetailLog.ui", self)
        self.data = data
        self.face_image.setScaledContents(True)
        # self.license_image.setScaledContents(True)
        self.ticket_button.clicked.connect(self.ticket)
        self.initData()

    def ticket(self):
        self.destroy()

    def initData(self):
        self.cam_id.setText(str(self.data['CARID']))
        self.behavior.setText(self.data['CARCOLOR'])

        if self.data['CARIMAGE'] is not None:
            self.face_image.setPixmap(self.data['CARIMAGE'])
        if self.data['LICENSEIMAGE'] is not None:
            self.license_image.setPixmap(self.data['LICENSEIMAGE'])

        self.preson_id.setText(self.data['LICENSENUMBER'])
        self.face_name.setText(self.data['LOCATION'])
        self.rule.setText(self.data['RULENAME'])

        self.close_button.clicked.connect(self.close)
        self.delete_button.clicked.connect(self.deleteRecord)

    def close(self):
        self.destroy()

    def deleteRecord(self):
        qm = QtWidgets.QMessageBox
        prompt = qm.question(self, '', "确定要删除吗?", qm.Yes | qm.No)
        if prompt == qm.Yes:
            self.destroy()
        else:
            pass

即使用：

loadUi(".ui", self)

4.4 美化界面：

现在界面是这样的：

我们可以用一些CSS语句来装饰部件，当然也可以用别人设计好的。

这里使用qdarkstyle来美化界面：

app.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())

美化后的效果：

5. UI功能开发：

5.1 绑定槽函数：

信号和槽是PyQt编程对象之间进行通信的机制。每个继承自QWideget的控件都支持信号与槽机制。信号发射时（发送请求），连接的槽函数就会自动执行（针对请求进行处理）。

以下为绑定文件选择按钮的槽函数的示例：

• 点击事件绑定：

self.file_btn.clicked.connect(
            lambda: self.getFile(self.file_edit))  # 文件选择槽函数绑定

• 文件选择函数：

def getFile(self, lineEdit):
        file_path = QFileDialog.getOpenFileName()[0]
        lineEdit.setText(file_path)  # 获取文件路径
        self.updateCamInfo(file_path)

这样的话点击 file_btn 按钮就会发出信号，从而运行绑定的函数：

5.2 在UI中显示视频：

这里主要是将图像逐帧放置到 QLabel 部件上。

首先我们需要将opencv的图像转为QImage：

def toQImage(self, img, height=800):
        if not height is None:
            img = imutils.resize(img, height=height)
        qformat = QImage.Format_Indexed8
        if len(img.shape) == 3:
            if img.shape[2] == 4:
                qformat = QImage.Format_RGBA8888
            else:
                qformat = QImage.Format_RGB888
        outImg = QImage(
            img.tobytes(), img.shape[1], img.shape[0], img.strides[0], qformat)
        outImg = outImg.rgbSwapped()
        return outImg

定义刷新时执行的函数：

def update_image(self, pt='face.jpg'):
        _, frame = self.vs.read()
        if frame is None:
            return
        frame = imutils.resize(frame, height=800)
        isChecked = {
    
    
            'region': self.region_checkbtn.isChecked(),
            'wall': self.wall_checkbtn.isChecked()
        }
        packet = self.processor.getProcessedImage(frame, isChecked, region_bbox2draw=self.region_bbox2draw)

        qimg0 = self.toQImage(packet['frame'], height=None)
        self.live_preview.setPixmap(QPixmap.fromImage(qimg0))

然后绑定计时器，规定每 50ms 刷新一次：

self.timer = QTimer(self)
self.timer.timeout.connect(self.update_image)
self.timer.start(50)

5.3 相应鼠标绘制区域：

这里使用PyQt5预置的响应函数：

def mouseReleaseEvent(self, event):  # 鼠标键释放时调用
        # 参数1：鼠标的作用对象；参数2：鼠标事件对象，用来保存鼠标数据
        self.unsetCursor()
        n = event.button()  # 用来判断是哪个鼠标健触发了事件【返回值：0  1  2  4】
        if n == 1:
            if self.region_checkbtn.isChecked() or self.wall_checkbtn.isChecked():
                x = event.x()  # 返回鼠标相对于窗口的x轴坐标
                y = event.y()  # 返回鼠标相对于窗口的y轴坐标

这样就可以获取到鼠标点击完释放时的位置，从而用来绘制危险区：

5.4 其他细节：

• 添加menu：

def add_setting_menu(self, settingsMenu):

        speed_menu = QMenu("更改模型", self)
        settingsMenu.addMenu(speed_menu)

        act = QAction('YOLOv3+DarkNet53', self)
        act.setStatusTip('YOLOv3+DarkNet53')
        speed_menu.addAction(act)

        act = QAction('YOLOv3+MobileNetV3', self)
        act.setStatusTip('YOLOv3+MobileNetV3')
        speed_menu.addAction(act)

        direct_menu = QMenu("更改阈值", self)
        settingsMenu.addMenu(direct_menu)

        act = QAction('阈值+0.05', self)
        act.setStatusTip('阈值+0.05')
        direct_menu.addAction(act)

        act = QAction('阈值-0.05', self)
        act.setStatusTip('阈值-0.05')
        direct_menu.addAction(act)

绑定细节框：

self.details_button.clicked.connect(self.showDetails)

def showDetails(self):
        window = DetailLogWindow(self.data, self)
        window.show()

from PyQt5 import QtWidgets
from PyQt5.QtWidgets import QMainWindow
from PyQt5.uic import loadUi

class DetailLogWindow(QMainWindow):
    def __init__(self, data, parent=None):
        super(DetailLogWindow, self).__init__(parent)
        loadUi("./data/UI/DetailLog.ui", self)
        self.data = data
        self.face_image.setScaledContents(True)
        # self.license_image.setScaledContents(True)
        self.ticket_button.clicked.connect(self.ticket)
        self.initData()

    def ticket(self):
        self.destroy()

    def initData(self):
        self.cam_id.setText(str(self.data['CARID']))
        self.behavior.setText(self.data['CARCOLOR'])

        if self.data['CARIMAGE'] is not None:
            self.face_image.setPixmap(self.data['CARIMAGE'])
        if self.data['LICENSEIMAGE'] is not None:
            self.license_image.setPixmap(self.data['LICENSEIMAGE'])

        self.preson_id.setText(self.data['LICENSENUMBER'])
        self.face_name.setText(self.data['LOCATION'])
        self.rule.setText(self.data['RULENAME'])

        self.close_button.clicked.connect(self.close)
        self.delete_button.clicked.connect(self.deleteRecord)

    def close(self):
        self.destroy()

    def deleteRecord(self):
        qm = QtWidgets.QMessageBox
        prompt = qm.question(self, '', "确定要删除吗?", qm.Yes | qm.No)
        if prompt == qm.Yes:
            self.destroy()
        else:
            pass

6. 部署方案：

6.1 端侧视频推流：

可以放在端侧设备上：

import cv2                                                       
import subprocess as sp                                          
import subprocess as sp                                          
                                                                 
rtmpUrl = "接收视频的服务器"                
camera_path = 0                                       
                                                                 
cap = cv2.VideoCapture(0)                              
                                                                 
# Get video information                                          
fps = int(cap.get(cv2.CAP_PROP_FPS))                             
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))                   
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))                 
# ffmpeg command                                                 
command = ['ffmpeg',                                             
           '-y',                                                 
           '-f', 'rawvideo',                                     
           '-vcodec', 'rawvideo',                                
           '-pix_fmt', 'bgr24',                                  
           '-s', "{}x{}".format(width, height),                  
           '-r', str(fps),                                       
           '-i', '-',                                            
           '-c:v', 'libx264',                                    
           '-pix_fmt', 'yuv420p',                                
           '-preset', 'ultrafast',                               
           '-f', 'flv',                                          
           rtmpUrl]                                              
# 管道配置                                                           
p = sp.Popen(command, stdin=sp.PIPE)                             
# read webcamera                                                 
while (cap.isOpened()):                                          
    ret, frame = cap.read()                                      
    # print("running......")                                     
    if not ret:                                                  
        print("Opening camera is failed")                        
        break                                                    
    p.stdin.write(frame.tostring())                              
                                                                 
                                                                 
return_value, frame = cap.read()                                 
                                                                 

6.2 Flask部署：

注意下面并非本项目代码，而是一个图像分类模型Flask部署的代码：

import datetime
import logging as rel_log
import os
import shutil
from datetime import timedelta
from paddlex import deploy
from flask import *

import core.main

UPLOAD_FOLDER = r'./uploads'

ALLOWED_EXTENSIONS = set(['png'])
app = Flask(__name__)
app.secret_key = 'secret!'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

werkzeug_logger = rel_log.getLogger('werkzeug')
werkzeug_logger.setLevel(rel_log.ERROR)

# 解决缓存刷新问题
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = timedelta(seconds=1)


# 添加header解决跨域
@app.after_request
def after_request(response):
    response.headers['Access-Control-Allow-Origin'] = '*'
    response.headers['Access-Control-Allow-Credentials'] = 'true'
    response.headers['Access-Control-Allow-Methods'] = 'POST'
    response.headers['Access-Control-Allow-Headers'] = 'Content-Type, X-Requested-With'
    return response


def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS


@app.route('/')
def hello_world():
    return redirect(url_for('static', filename='./index.html'))


@app.route('/upload', methods=['GET', 'POST'])
def upload_file():
    file = request.files['file']
    print(datetime.datetime.now(), file.filename)
    if file and allowed_file(file.filename):
        src_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
        file.save(src_path)
        shutil.copy(src_path, './tmp/ct')
        image_path = os.path.join('./tmp/ct', file.filename)
        print(src_path, image_path)
        pid, image_info = core.main.c_main(image_path, current_app.model)
        return jsonify({
    
    'status': 1,
                        'image_url': 'http://127.0.0.1:5003/tmp/ct/' + pid,
                        'draw_url': 'http://127.0.0.1:5003/tmp/draw/' + pid,
                        'image_info': image_info
                        })

    return jsonify({
    
    'status': 0})


@app.route("/download", methods=['GET'])
def download_file():
    # 需要知道2个参数, 第1个参数是本地目录的path, 第2个参数是文件名(带扩展名)
    return send_from_directory('data', 'testfile.zip', as_attachment=True)


# show photo
@app.route('/tmp/<path:file>', methods=['GET'])
def show_photo(file):
    if request.method == 'GET':
        if not file is None:
            image_data = open(f'tmp/{file}', "rb").read()
            response = make_response(image_data)
            response.headers['Content-Type'] = 'image/png'
            return response

if __name__ == '__main__':
    with app.app_context():
        current_app.model = deploy.Predictor(
            './core/net/inference_model', use_gpu=True)
    app.run(host='127.0.0.1', port=5003, debug=True)

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